Self-adaptive distributed energy storage group dispatching and group control method and system

Abstract

The invention provides an adaptive distributed energy storage group scheduling and group control method and system, and the method comprises the steps: carrying out the clustering based on the current attenuation degree index, SOC adjustment rate index and communication delay index of each distributed energy storage unit in a transformer area, and the electrical distance between each distributed energy storage unit and other nodes, and obtaining a plurality of virtual energy storage clusters; calculating a performance index of each virtual energy storage cluster based on the performance parameter of the energy storage unit in each virtual energy storage cluster; inputting the performance index of each virtual energy storage cluster into a multi-objective optimization scheduling model to obtain a scheduling instruction of each virtual energy storage cluster; the scheduling instruction of each virtual energy storage cluster is decomposed to each energy storage unit and executed; according to the method, based on multi-dimensional performance index clustering, the internal homogeneity of virtual energy storage is high, the instruction execution consistency is high, and the regulation and control precision is improved; the on-site consumption rate of new energy is remarkably improved and the line loss is reduced through optimal scheduling; according to the method, online regrouping under dynamic changes such as energy storage aging and communication fluctuation is supported, and long-term effectiveness is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power operation and control, specifically to an adaptive distributed energy storage group control method and system. Background Technology

[0002] As the penetration rate of distributed photovoltaic (PV) and wind power, among other renewable energy sources, continues to increase in distribution networks, the problem of source-load mismatch in time and space is becoming increasingly prominent, leading to frequent voltage exceedances, power backflow, and curtailment of solar and wind power in some areas. Distributed energy storage, as a key flexible regulation resource, possesses rapid response, bidirectional regulation, and time-space transfer capabilities. By implementing coordinated group control of distributed energy storage, it is possible to effectively smooth out renewable energy fluctuations, support peak loads, and optimize power flow distribution, thereby enhancing the distribution network's capacity to support high proportions of renewable energy and improving operational safety.

[0003] However, current mainstream distributed energy storage group control methods mostly adopt static grouping or simple aggregation strategies, failing to fully consider the heterogeneity of each energy storage unit in terms of battery degradation, dynamic SOC adjustment rate, communication latency, and electrical topology location. This "one-size-fits-all" control approach easily leads to inconsistent responses within the cluster and large deviations in the execution of scheduling commands, making it difficult to achieve efficient local consumption of new energy and precise suppression of network losses. Especially in variable operating environments, the lack of perception and utilization of individual energy storage performance differences severely restricts the overall control efficiency of the group control system. Summary of the Invention

[0004] To address the problem that existing technologies lack the ability to perceive and utilize the performance differences of individual energy storage units under varying operating environments, which severely restricts the overall control efficiency of group control systems, this invention proposes an adaptive distributed energy storage group control method, comprising: Based on the current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes of each distributed energy storage unit in the transformer area, multiple virtual energy storage clusters are obtained. Based on the performance parameters of the energy storage units within each virtual energy storage cluster, the performance indicators of each virtual energy storage cluster are calculated. The performance indicators of each virtual energy storage cluster are input into the multi-objective optimization scheduling model to obtain the scheduling instructions for each virtual energy storage cluster. The scheduling instructions for each virtual energy storage cluster are decomposed and executed to each energy storage unit. The multi-objective optimization scheduling model is constructed with the objectives of maximizing the local consumption of new energy and minimizing line loss.

[0005] Preferably, the clustering based on the current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes of each distributed energy storage unit within the transformer area yields multiple virtual energy storage clusters, including: The comprehensive characteristic value of each energy storage unit is obtained by weighting and fusing the current attenuation level index, SOC adjustment rate index, and communication delay index of each distributed energy storage unit in the transformer area. Clustering is performed on the comprehensive characteristic values ​​of each energy storage unit to obtain multiple virtual energy storage clusters.

[0006] Preferably, the formula for calculating the comprehensive feature value is as follows:

[0007] In the formula, Indicates the first i The comprehensive characteristic value of each energy storage unit, Indicates the first i The degradation index of each energy storage unit Indicates the first i SOC regulation rate of each energy storage unit Indicates the first i Communication delay of each energy storage unit Indicates the first i The energy storage unit to the first j Electrical distance of each energy storage unit N This represents the total number of energy storage units. As the weight of the attenuation index, As the weight of the SOC adjustment rate, As the weight for communication delay, The weight of the electrical distance.

[0008] Preferably, the performance indicators of the virtual energy storage cluster include one or more of the following: total available capacity, maximum charge / discharge power, equivalent SOC, or communication reliability factor; The formula for calculating the total available capacity is as follows:

[0009] The formula for calculating the maximum charge / discharge power is as follows:

[0010] The formula for calculating the equivalent SOC is as follows:

[0011] The formula for calculating the communication reliability factor is as follows:

[0012] In the formula, For the first k Total available capacity of the virtual energy storage clusters For the first i The capacity of each energy storage unit For the first i The degradation index of each energy storage unit Indicates the first i The energy storage unit belongs to the first k A virtual energy storage cluster, For the first k The maximum charging / discharging power of a virtual energy storage cluster For the first i The maximum charging / discharging power of each energy storage unit No. k The equivalent SOC of a virtual energy storage cluster For the first i SOC of each energy storage unit For the first k Communication reliability factor of a virtual energy storage cluster For the first k Average latency of a virtual energy storage cluster α This is the delay factor.

[0013] Preferably, the construction process of the multi-objective optimization scheduling model includes: An objective function is established with the goals of maximizing the local consumption of new energy and minimizing line loss. A multi-objective optimization scheduling model is constructed using virtual energy storage power constraints, SOC dynamic constraints, power balance constraints, and voltage safety constraints as constraints.

[0014] Preferably, the objective function is calculated as follows:

[0015] In the formula, G Describe the objective function. Indicates line loss. This indicates the local consumption rate of new energy sources. This indicates the weight of the line loss.

[0016] Based on the same inventive concept, this application also provides an adaptive distributed energy storage group dispatch and control system, comprising: The cluster partitioning module is used to cluster the distributed energy storage units in the transformer area based on their current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes, thereby obtaining multiple virtual energy storage clusters. The performance metrics module is used to determine the performance metrics of each virtual energy storage cluster based on the performance parameters of the energy storage units within each virtual energy storage cluster. The instruction calculation module is used to input the performance indicators of each virtual energy storage cluster into the multi-objective optimization scheduling model to obtain the scheduling instructions for each virtual energy storage cluster. The instruction decomposition module is used to decompose the scheduling instructions of each virtual energy storage cluster to each energy storage unit and execute them. The multi-objective optimization scheduling model is constructed with the objectives of maximizing the local consumption of new energy and minimizing line loss.

[0017] Preferably, the cluster partitioning module is specifically used for: The comprehensive characteristic value of each energy storage unit is obtained by weighting and fusing the current attenuation level index, SOC adjustment rate index, and communication delay index of each distributed energy storage unit in the transformer area. Clustering is performed on the comprehensive characteristic values ​​of each energy storage unit to obtain multiple virtual energy storage clusters.

[0018] Preferably, the formula for calculating the comprehensive feature value in the cluster partitioning module is as follows:

[0019] In the formula, Indicates the first i The comprehensive characteristic value of each energy storage unit, Indicates the first i The degradation index of each energy storage unit Indicates the first i SOC regulation rate of each energy storage unit Indicates the first i Communication delay of each energy storage unit Indicates the first i The energy storage unit to the first j Electrical distance of each energy storage unit N This represents the total number of energy storage units. As the weight of the attenuation index, As the weight of the SOC adjustment rate, As the weight for communication delay, The weight of the electrical distance.

[0020] Preferably, the performance indicators of the virtual energy storage cluster in the performance indicator module include one or more of the following: total available capacity, maximum charge / discharge power, equivalent SOC, or communication reliability factor. The formula for calculating the total available capacity is as follows:

[0021] The formula for calculating the maximum charge / discharge power is as follows:

[0022] The formula for calculating the equivalent SOC is as follows:

[0023] The formula for calculating the communication reliability factor is as follows:

[0024] In the formula, For the first k Total available capacity of the virtual energy storage clusters For the first i The capacity of each energy storage unit For the first i The degradation index of each energy storage unit Indicates the first i The energy storage unit belongs to the first k A virtual energy storage cluster, For the first k The maximum charging / discharging power of a virtual energy storage cluster For the first i The maximum charging / discharging power of each energy storage unit No. k The equivalent SOC of a virtual energy storage cluster For the first i SOC of each energy storage unit For the first k Communication reliability factor of a virtual energy storage cluster For the first k Average latency of a virtual energy storage cluster α This is the delay factor.

[0025] Preferably, the system also includes a modeling module, specifically used for: An objective function is established with the goals of maximizing the local consumption of new energy and minimizing line loss. A multi-objective optimization scheduling model is constructed using virtual energy storage power constraints, SOC dynamic constraints, power balance constraints, and voltage safety constraints as constraints.

[0026] Preferably, the objective function in the modeling module is calculated as follows:

[0027] In the formula, G Describe the objective function. Indicates line loss. This indicates the local consumption rate of new energy sources. This indicates the weight of the line loss.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides an adaptive distributed energy storage cluster control method and system, comprising: clustering based on the current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes of each distributed energy storage unit within a distribution area to obtain multiple virtual energy storage clusters; calculating the performance index of each virtual energy storage cluster based on the performance parameters of the energy storage units within each virtual energy storage cluster; inputting the performance index of each virtual energy storage cluster into a multi-objective optimization scheduling model to obtain scheduling instructions for each virtual energy storage cluster; decomposing the scheduling instructions of each virtual energy storage cluster to each energy storage unit and executing them; wherein, the multi-objective optimization scheduling model is constructed with the objectives of maximizing local consumption of new energy and minimizing line loss; this invention, based on multi-dimensional performance index clustering, makes the virtual energy storage highly homogeneous, the instruction execution consistency high, and improves the control accuracy; through optimized scheduling, it significantly improves the local consumption rate of new energy and reduces line loss; this invention supports online regrouping under dynamic changes such as energy storage aging and communication fluctuations, ensuring long-term effectiveness. Attached Figure Description

[0029] Figure 1 A flowchart of an adaptive distributed energy storage group regulation and control method provided by the present invention; Figure 2 The diagram shows the result of an adaptive distributed energy storage group control system provided by this invention. Detailed Implementation

[0030] This invention aims to address the lack of perception and utilization of individual performance differences in existing distributed energy storage group control strategies. It proposes an adaptive distributed energy storage group control method and system, constructs highly cohesive and loosely coupled virtual energy storage units, and on this basis, realizes dynamic scheduling control with the joint optimization objectives of maximizing local consumption of new energy and minimizing supply and sales line losses.

[0031] Example 1: An adaptive distributed energy storage group dispatch and control method, such as Figure 1 As shown, it includes: Step 1: Cluster the distributed energy storage units within the distribution area based on their current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes to obtain multiple virtual energy storage clusters; Step 2: Calculate the performance indicators of each virtual energy storage cluster based on the performance parameters of the energy storage units within each virtual energy storage cluster; Step 3: Input the performance indicators of each virtual energy storage cluster into the multi-objective optimization scheduling model to obtain the scheduling instructions for each virtual energy storage cluster; Step 4: Decompose the scheduling instructions of each virtual energy storage cluster to each energy storage unit and execute them; Among them, the multi-objective optimization scheduling model is constructed with the objectives of maximizing the local consumption of new energy and minimizing line loss.

[0032] Step 1 specifically includes: In each scheduling execution cycle, the comprehensive characteristic value of each energy storage unit is obtained by weighted fusion based on the current attenuation level index, SOC adjustment rate index, and communication delay index of each distributed energy storage unit in the distribution area. Clustering is performed on the comprehensive characteristic values ​​of each energy storage unit to obtain multiple virtual energy storage clusters.

[0033] The formula for calculating the comprehensive eigenvalue is as follows:

[0034] In the formula, Indicates the first i The comprehensive characteristic value of each energy storage unit, Indicates the first i The degradation index of each energy storage unit Indicates the first i SOC regulation rate of each energy storage unit Indicates the first i Communication delay of each energy storage unit Indicates the first i The energy storage unit to the first j Electrical distance of each energy storage unit N This represents the total number of energy storage units. As the weight of the attenuation index, As the weight of the SOC adjustment rate, As the weight for communication delay, The weight of the electrical distance.

[0035] In step 1, K-means or spectral clustering algorithms can be used, based on... The energy storage units are dynamically clustered to form several Virtual Energy Storage Clusters (VESCs). Each VESC member has similar overall performance and is feasible for coordinated control.

[0036] In step 2, the performance indicators of the virtual energy storage cluster include one or more of the following: total available capacity, maximum charge / discharge power, equivalent SOC, or communication reliability factor. The formula for calculating the total available capacity is as follows:

[0037] The formula for calculating the maximum charge / discharge power is as follows:

[0038] The formula for calculating the equivalent SOC is as follows:

[0039] The formula for calculating the communication reliability factor is as follows:

[0040] In the formula, For the first k Total available capacity of the virtual energy storage clusters For the first i The capacity of each energy storage unit For the first i The degradation index of each energy storage unit Indicates the first i The energy storage unit belongs to the first k Virtual energy storage cluster , For the first k The maximum charging / discharging power of a virtual energy storage cluster For the first i The maximum charging / discharging power of each energy storage unit No. k The equivalent SOC of a virtual energy storage cluster For the first i SOC of each energy storage unit For the first k Communication reliability factor of a virtual energy storage cluster For the first k Average latency of a virtual energy storage cluster α This is the delay factor.

[0041] The construction process of the multi-objective optimization scheduling model used in step 3 includes: An objective function is established with the goals of maximizing the local consumption of new energy and minimizing line loss. A multi-objective optimization scheduling model is constructed using virtual energy storage power constraints, SOC dynamic constraints, power balance constraints, and voltage safety constraints as constraints.

[0042] The objective function is calculated as follows:

[0043] In the formula, G Describe the objective function. Indicates line loss. This indicates the local consumption rate of new energy sources. This indicates the weight of the line loss. The line loss is calculated from the power flow.

[0044] The expression for the virtual energy storage power constraint is as follows:

[0045]

[0046] In the formula, Indicates the first k The charging power of a virtual energy storage cluster Indicates the first k The maximum charging power of a virtual energy storage cluster Indicates the first k The discharge power of a virtual energy storage cluster Indicates the first k The maximum discharge power of a virtual energy storage cluster.

[0047] The expression for the SOC dynamic constraint is as follows:

[0048] In the formula, Indicates the first k The minimum SOC of a virtual energy storage cluster Indicates the first k The maximum SOC of a virtual energy storage cluster.

[0049] The expression for the power balance constraint is:

[0050] In the formula, This represents the sum of all loads in the transformer area. This represents the sum of the discharge power of the energy storage units. This represents the total charging power of the energy storage units. It represents the total power generation of new energy sources. It represents the power interacting with the power grid; it is positive when purchased from the grid and negative when output to the grid.

[0051] The expression for voltage safety constraints is:

[0052] In the formula, Indicates the number of Taiwan districts n The voltage of each node, This represents the minimum voltage value. This indicates the maximum voltage value.

[0053] Example 2: Based on the same inventive concept, this invention also provides an adaptive distributed energy storage group dispatch and control system, such as... Figure 2 As shown, it includes: The cluster partitioning module is used to cluster the distributed energy storage units in the transformer area based on their current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes, thereby obtaining multiple virtual energy storage clusters. The performance metrics module is used to determine the performance metrics of each virtual energy storage cluster based on the performance parameters of the energy storage units within each virtual energy storage cluster. The instruction calculation module is used to input the performance indicators of each virtual energy storage cluster into the multi-objective optimization scheduling model to obtain the scheduling instructions for each virtual energy storage cluster. The instruction decomposition module is used to decompose the scheduling instructions of each virtual energy storage cluster to each energy storage unit and execute them. The multi-objective optimization scheduling model is constructed with the objectives of maximizing the local consumption of new energy and minimizing line loss.

[0054] Preferably, the cluster partitioning module is specifically used for: The comprehensive characteristic value of each energy storage unit is obtained by weighting and fusing the current attenuation level index, SOC adjustment rate index, and communication delay index of each distributed energy storage unit in the transformer area. Clustering is performed on the comprehensive characteristic values ​​of each energy storage unit to obtain multiple virtual energy storage clusters.

[0055] Preferably, the formula for calculating the comprehensive feature value in the cluster partitioning module is as follows:

[0056] In the formula, Indicates the first i The comprehensive characteristic value of each energy storage unit, Indicates the first i The degradation index of each energy storage unit Indicates the first i SOC regulation rate of each energy storage unit Indicates the first i Communication delay of each energy storage unit Indicates the first i The energy storage unit to the first j Electrical distance of each energy storage unit N This represents the total number of energy storage units. As the weight of the attenuation index, As the weight of the SOC adjustment rate, As the weight for communication delay, The weight of the electrical distance.

[0057] Preferably, the performance indicators of the virtual energy storage cluster in the performance indicator module include one or more of the following: total available capacity, maximum charge / discharge power, equivalent SOC, or communication reliability factor. The formula for calculating the total available capacity is as follows:

[0058] The formula for calculating the maximum charge / discharge power is as follows:

[0059] The formula for calculating the equivalent SOC is as follows:

[0060] The formula for calculating the communication reliability factor is as follows:

[0061] In the formula, For the first k Total available capacity of the virtual energy storage clusters For the first i The capacity of each energy storage unit For the first i The degradation index of each energy storage unit Indicates the first i The energy storage unit belongs to the first k A virtual energy storage cluster, For the first k The maximum charging / discharging power of a virtual energy storage cluster For the first i The maximum charging / discharging power of each energy storage unit No. k The equivalent SOC of a virtual energy storage cluster For the first i SOC of each energy storage unit For the first k Communication reliability factor of a virtual energy storage cluster For the first k Average latency of a virtual energy storage cluster α This is the delay factor.

[0062] Preferably, the system also includes a modeling module, specifically used for: An objective function is established with the goals of maximizing the local consumption of new energy and minimizing line loss. A multi-objective optimization scheduling model is constructed using virtual energy storage power constraints, SOC dynamic constraints, power balance constraints, and voltage safety constraints as constraints.

[0063] Preferably, the objective function in the modeling module is calculated as follows:

[0064] In the formula, G Describe the objective function. Indicates line loss. This indicates the local consumption rate of new energy sources. This indicates the weight of the line loss.

[0065] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. An adaptive distributed energy storage group dispatch and control method, characterized in that, include: Based on the current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes of each distributed energy storage unit in the transformer area, multiple virtual energy storage clusters are obtained. Based on the performance parameters of the energy storage units within each virtual energy storage cluster, the performance indicators of each virtual energy storage cluster are calculated. The performance indicators of each virtual energy storage cluster are input into the multi-objective optimization scheduling model to obtain the scheduling instructions for each virtual energy storage cluster. The scheduling instructions for each virtual energy storage cluster are decomposed and executed to each energy storage unit. The multi-objective optimization scheduling model is constructed with the objectives of maximizing the local consumption of new energy and minimizing line loss.

2. The adaptive distributed energy storage group dispatch and control method as described in claim 1, characterized in that, The method involves clustering based on the current attenuation level, SOC adjustment rate, communication latency, and electrical distance to other nodes of each distributed energy storage unit within the transformer area to obtain multiple virtual energy storage clusters, including: The comprehensive characteristic value of each energy storage unit is obtained by weighting and fusing the current attenuation level index, SOC adjustment rate index, and communication delay index of each distributed energy storage unit in the transformer area. Clustering is performed on the comprehensive characteristic values ​​of each energy storage unit to obtain multiple virtual energy storage clusters.

3. The adaptive distributed energy storage group dispatch and control method as described in claim 2, characterized in that, The formula for calculating the comprehensive feature value is as follows: In the formula, Indicates the first i The comprehensive characteristic value of each energy storage unit, Indicates the first i The degradation index of each energy storage unit Indicates the first i SOC regulation rate of each energy storage unit Indicates the first i Communication delay of each energy storage unit Indicates the first i The energy storage unit to the first j Electrical distance of each energy storage unit N This represents the total number of energy storage units. As the weight of the attenuation index, As the weight of the SOC adjustment rate, As the weight for communication delay, The weight of the electrical distance.

4. The adaptive distributed energy storage group dispatch and control method as described in claim 1, characterized in that, The performance indicators of the virtual energy storage cluster include one or more of the following: total available capacity, maximum charge / discharge power, equivalent SOC, or communication reliability factor. The formula for calculating the total available capacity is as follows: The formula for calculating the maximum charge / discharge power is as follows: The formula for calculating the equivalent SOC is as follows: The formula for calculating the communication reliability factor is as follows: In the formula, For the first k Total available capacity of the virtual energy storage clusters For the first i The capacity of each energy storage unit For the first i The degradation index of each energy storage unit Indicates the first i The energy storage unit belongs to the first k A virtual energy storage cluster, For the first k The maximum charging / discharging power of a virtual energy storage cluster For the first i The maximum charging / discharging power of each energy storage unit No. k The equivalent SOC of a virtual energy storage cluster For the first i SOC of each energy storage unit For the first k Communication reliability factor of a virtual energy storage cluster For the first k Average latency of a virtual energy storage cluster α This is the delay factor.

5. The adaptive distributed energy storage group dispatch and control method as described in claim 1, characterized in that, The construction process of the multi-objective optimization scheduling model includes: An objective function is established with the goals of maximizing the local consumption of new energy and minimizing line loss. A multi-objective optimization scheduling model is constructed using virtual energy storage power constraints, SOC dynamic constraints, power balance constraints, and voltage safety constraints as constraints.

6. The adaptive distributed energy storage group dispatch and control method as described in claim 5, characterized in that, The objective function is calculated as follows: In the formula, G Describe the objective function. Indicates line loss. This indicates the local consumption rate of new energy sources. This indicates the weight of the line loss.

7. An adaptive distributed energy storage group dispatch and control system, characterized in that, include: The cluster partitioning module is used to cluster the distributed energy storage units in the transformer area based on their current attenuation level, SOC adjustment rate, communication delay, and electrical distance to other nodes, thereby obtaining multiple virtual energy storage clusters. The performance metrics module is used to determine the performance metrics of each virtual energy storage cluster based on the performance parameters of the energy storage units within each virtual energy storage cluster. The instruction calculation module is used to input the performance indicators of each virtual energy storage cluster into the multi-objective optimization scheduling model to obtain the scheduling instructions for each virtual energy storage cluster. The instruction decomposition module is used to decompose the scheduling instructions of each virtual energy storage cluster to each energy storage unit and execute them. The multi-objective optimization scheduling model is constructed with the objectives of maximizing the local consumption of new energy and minimizing line loss.

8. The adaptive distributed energy storage group dispatch and control system as described in claim 7, characterized in that, The cluster partitioning module is specifically used for: The comprehensive characteristic value of each energy storage unit is obtained by weighting and fusing the current attenuation level index, SOC adjustment rate index, and communication delay index of each distributed energy storage unit in the transformer area. Clustering is performed on the comprehensive characteristic values ​​of each energy storage unit to obtain multiple virtual energy storage clusters.

9. The adaptive distributed energy storage group dispatch and control system as described in claim 8, characterized in that, The formula for calculating the comprehensive feature value in the cluster partitioning module is as follows: In the formula, Indicates the first i The comprehensive characteristic value of each energy storage unit, Indicates the first i The degradation index of each energy storage unit Indicates the first i SOC regulation rate of each energy storage unit Indicates the first i Communication delay of each energy storage unit Indicates the first i The energy storage unit to the first j Electrical distance of each energy storage unit N This represents the total number of energy storage units. As the weight of the attenuation index, As the weight of the SOC adjustment rate, As the weight for communication delay, The weight of the electrical distance.

10. The adaptive distributed energy storage group dispatch and control system as described in claim 7, characterized in that, The performance indicators of the virtual energy storage cluster in the performance indicator module include one or more of the following: total available capacity, maximum charge / discharge power, equivalent SOC or communication reliability factor; The formula for calculating the total available capacity is as follows: The formula for calculating the maximum charge / discharge power is as follows: The formula for calculating the equivalent SOC is as follows: The formula for calculating the communication reliability factor is as follows: In the formula, For the first k Total available capacity of the virtual energy storage clusters For the first i The capacity of each energy storage unit For the first i The degradation index of each energy storage unit Indicates the first i The energy storage unit belongs to the first k A virtual energy storage cluster, For the first k The maximum charging / discharging power of a virtual energy storage cluster For the first i The maximum charging / discharging power of each energy storage unit No. k The equivalent SOC of a virtual energy storage cluster For the first i SOC of each energy storage unit For the first k Communication reliability factor of a virtual energy storage cluster For the first k Average latency of a virtual energy storage cluster α This is the delay factor.

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